Introduction
R is a powerful statistical engine comes with extensive libraries and innovative methodology implementation. On the other hand, Tableau is a popular business intelligence tool to visualize graphs and chart in fairly easy and straight forward manner. Tableau and R complement each other in areas where heavy data crunching is needed for visualization or when geospatial visualization and geodata manipulation are both needed. In this talk, two cases will be discussed to highlight the complementary merits of both software: dot map and choropleth map.
Mapping
Geospatial community is familiar with a laundry list of libraries and packages to be attached before perform spatial statistics. However, the users’ contribution to the CRAN mirrors has made monumental advances in bridging academic area and industry, shorten the gap between research and implementation. Both essential geographic data management and analysis can be performed readily in R. These advantages empower R as a leading open-source programming language against expensive commercial software in the market.
Despite extensive libraries and state-of-the-art algorithm available in R, visualization is not always praised as the language’s forte. For example, the omnipotent plot command to display kernel density estimation (KDE) in R challenges analysts to be well conversant with the location details to answer the key question: where is the hotspot? User interactivity functionality is another hurdle to overcome. The reactive graph requires considerably substantial amount of codes and sometimes cumbersome JavaScript to carry out simple customization or overlay dots on the base map. Some may argue that the communicating the point pattern process result with R is not an easy task. It is simply not compelling and visually aesthetic enough. #Integrating R and Tableau to produce map for spatial statistics Even though Tableau is hardly mentioned when it comes to mapping and geospatial, the user friendliness and interactivity are its selling points. Presenter has found majority of his mapping requirements satisfied by Tableau’s functionalities. The talk will detail two applications where R and Tableau can come together and greatly complement each other.
- Display a set of point objects on a map: in this example, a collection of coordinates will be projected to the standard WGS 1984 Coordinate Reference System and Tableau acts as a base map to overlay the points.
- Juxtapose and highlight region with using areal data: the task requires analyst to map area objects, compare different regions and highlight member of top and bottom groups according a ranking parameter. Technically speaking, a choropleth map is needed.
